The Structure of Information Networks
Computer Science / Information Science 6850
Time: Mondays and Wednesdays at 10:10-11:25
Place: 110 Hollister Hall
The past two decades have seen a convergence of social and technological
networks, with systems such as the World Wide Web characterized
by the interplay between rich information content, the
millions of individuals and organizations who create it,
and the technology that supports it.
This course covers recent research on the structure and analysis of
such networks, and on models that abstract their basic properties.
Topics include combinatorial and probabilistic techniques for
link analysis, centralized and decentralized search algorithms,
network models based on random graphs, and connections with work
in the social sciences.
The course prerequisites include introductory-level background
in algorithms, graphs, probability, and linear algebra, as well
as some basic programming experience (to be able to manipulate
The work for the course
will consist primarily of
two problem sets, a short reaction paper,
and a more substantial project.
(1) Random Graphs and Small-World Properties
A major goal of the course is to illustrate
how networks across a variety of domains exhibit
common structure at a qualitative level.
One area in which this arises is in the study
of `small-world properties' in networks:
many large networks have short paths between most pairs of nodes,
even though they are highly clustered at a local level,
and they are searchable in the sense that one can
navigate to specified target nodes without global knowledge.
These properties turn out to provide insight into the
structure of large-scale social networks, and,
in a different direction, to have applications
to the design of decentralized peer-to-peer systems.
- Small-world experiments in social networks.
- Basic Random Graph Models, and the Consequences of Expansion.
- Decentralized Search in Networks.
- Decentralized Search in Peer-to-Peer Systems
- Nearest-Neighbor Search in Metric Spaces
(2) Cascading Behavior in Networks
We can think of a network as a large circulatory system,
through which information continuously flows.
This diffusion of information can happen rapidly or slowly;
it can be disastrous -- as in a panic or cascading failure --
or beneficial -- as in the spread of an innovation.
Work in several areas has proposed models for such processes,
and investigated when a network is more or less susceptible
to their spread.
This type of diffusion or cascade process
can also be used as a design principle for network protocols.
This leads to the idea of
epidemic algorithms, also called gossip-based algorithms,
in which information is propagated through a collection
of distributed computing hosts, typically using some
form of randomization.
- Models of Collective Action.
- Threshold-Based Models of Diffusion in Networks.
- Simple Probabilistic Models of Contagion.
- Finding Influential Sets of Nodes.
(3) Spectral Analysis and Random Walks in Networks
One can gain a lot of insight into the structure of a network
by analzing the
eigenvalues and eigenvectors of its adjacency matrix.
The connection between spectral parameters and
the more combinatorial properties of networks and datasets
is a subtle issue, and
while many results have been established about this connection,
it is still not fully understood.
This connection has also led to a number of applications,
including the development of link analysis algorithms for Web search.
- Link Analysis and Web Search